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Robert DeMaria.  Motivation  Objective  Data  Center-Fixing Method  Evaluation Method  Results  Conclusion.

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Presentation on theme: "Robert DeMaria.  Motivation  Objective  Data  Center-Fixing Method  Evaluation Method  Results  Conclusion."— Presentation transcript:

1 Robert DeMaria

2  Motivation  Objective  Data  Center-Fixing Method  Evaluation Method  Results  Conclusion

3  Only west Atlantic has routine hurricane hunter aircraft for finding storm centers  Satellite data used subjectively to find centers across the globe  Improvements to accuracy in real- time highly desirable sos.noaa.gov/Education/tracking.html

4  Geostationary satellites produce Infrared(IR) every 15 Minutes  Forecast produced every 6 hours  Due to time constraints, most of these images are unused  Automatic method for estimating tropical cyclone location is highly desirable

5  Tropical cyclones are roughly circular  Use Circular Hough Transform (CHT) to produce estimate for tropical cyclone location by finding circles in IR imagery  Compare accuracy to National Hurricane Center real-time center-fix

6  2D Image of Temperature ◦ Created every 15 minutes

7  A-Deck: Real-time estimate of position, velocity, wind speed, etc. ◦ Updated every 6 hours  Best-Track: Improved a-deck data available after end of season

8  Find a-deck position ◦ Given the time an IR image was created, look up most recent a-deck information and extrapolate position to IR image time  Subset of IR image used ◦ Center image on a-deck position ◦ Image reduced to area around storm/area around eye ◦ Background removed from cloud shield using temperature threshold

9  IR after subsect & thresholding:

10  Laplacian of image performed to find edge pixels

11  Circular Hough Transform performed for a range of radii on image  Gaussian fit performed on accumulation space to produce center location

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13  For each time in best-track, find most recent IR image  Estimate if eye is present in image ◦ If it is then perform center-fix searching for radii roughly the size of an eye ◦ If not, perform center-fix searching for radii roughly the size of the entire storm  Error calculated as CHT center-fix distance from best-track location  Compare error to that of the a-deck position

14 Katrina 08/29/ Earl 09/02/ Charley 08/13/ Katrina 08/25/ Ericka 09/02/ Sandy 10/19/ No Eye Cases Eye Cases

15  Charley 2004 – Very small but intense hurricane  Katrina 2005 – Classic large, intense hurricane  Ericka 2009 – Very disorganized weak tropical cyclone, did not make it to hurricane strength  Earl 2010 – Strong hurricane in higher latitudes  Sandy 2012 – Unusually large but only moderate strength, non-classical hurricane structure

16  Mean a-deck error: 42 km  Mean CHT error: 91 km  Bias X: 6 km  Bias Y: 8.5 km  Bias Explained by Parallax

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19  Strong Circular Eye Greatly Improves Accuracy ◦ Eye Mean Error: 54 km ◦ No Eye Mean Error: 127 km ◦ Strong circular eyes are fairly rare

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21  Did not improve real-time center fix  Rotational center may not be in center of cloud features: CHT may not be well suited to large-scale images  CHT may be useful when an eye is present

22  Use time-series information to improve  Combine with information about vertical shear  Improve eye estimation technique


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